# Fair and Unbiased Algorithmic Decision Making: Current State and Future Challenges

arXiv: https://arxiv.org/abs/1901.04730 · HTML: https://mlsanity.com/paper/1901.04730/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2019.

## Built on by (most cited first, 5 of 5)

- [Auditing of AI: Legal, Ethical and Technical Approaches](https://mlsanity.com/paper/2407.06235/) (2024)
- [Gender Bias in Text: Labeled Datasets and Lexicons](https://mlsanity.com/paper/2201.08675/) (2022)
- [Liability regimes in the age of AI: a use-case driven analysis of the burden of proof](https://mlsanity.com/paper/2211.01817/) (2022)
- [Attribute Annotation and Bias Evaluation in Visual Datasets for Autonomous Driving](https://mlsanity.com/paper/2312.06306/) (2023)
- [Disentangling Dialect from Social Bias via Multitask Learning to Improve Fairness](https://mlsanity.com/paper/2406.09977/) (2024)

## Abstract (arXiv)

Machine learning algorithms are now frequently used in sensitive contexts that substantially affect the course of human lives, such as credit lending or criminal justice. This is driven by the idea that `objective' machines base their decisions solely on facts and remain unaffected by human cognitive biases, discriminatory tendencies or emotions. Yet, there is overwhelming evidence showing that algorithms can inherit or even perpetuate human biases in their decision making when they are based on data that contains biased human decisions. This has led to a call for fairness-aware machine learning. However, fairness is a complex concept which is also reflected in the attempts to formalize fairness for algorithmic decision making. Statistical formalizations of fairness lead to a long list of criteria that are each flawed (or harmful even) in different contexts. Moreover, inherent tradeoffs in these criteria make it impossible to unify them in one general framework. Thus, fairness constraints in algorithms have to be specific to the domains to which the algorithms are applied. In the future, research in algorithmic decision making systems should be aware of data and developer biases and add a focus on transparency to facilitate regular fairness audits.